{"id":"W2920914266","doi":"10.1111/cge.13531","title":"Genetic counselors' preferences for coverage of preimplantation genetic diagnosis: A discrete choice experiment","year":2019,"lang":"en","type":"article","venue":"Clinical Genetics","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; SickKids Foundation; Hospital for Sick Children; Institute for Clinical Evaluative Sciences; University of Calgary; Trillium Health Centre","funders":"Institute of Health Services and Policy Research; Canadian Institutes of Health Research; Hospital for Sick Children","keywords":"Multinomial logistic regression; Genetic testing; Logistic regression; Scope (computer science); Genetic counseling; Actuarial science; Preimplantation genetic diagnosis; Fertility; Mixed logit; Discrete choice; Family history; Psychology; Medicine; Demography; Environmental health; Business; Econometrics; Computer science; Economics; Biology; Population; Genetics; Pregnancy; Surgery","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006206613,0.0004705245,0.0006929883,0.0003184868,0.001119146,0.001788928,0.0007356229,0.001307316,0.007006891],"category_scores_gemma":[0.01637222,0.0003272974,0.0005062083,0.0004292167,0.0009923248,0.0004828221,0.0004653479,0.001449199,0.0003699541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003248836,"about_ca_system_score_gemma":0.005291978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08233593,"about_ca_topic_score_gemma":0.1010162,"domain_scores_codex":[0.9963319,0.001963185,0.0001895895,0.000257848,0.000701365,0.0005562357],"domain_scores_gemma":[0.9782146,0.01748148,0.001615804,0.0007351681,0.0005583732,0.001394673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1613144,0.1855995,0.3428851,0.001243192,0.000877517,0.001349635,0.03860074,0.03537187,0.07131471,0.01494746,0.008051585,0.1384443],"study_design_scores_gemma":[0.0418322,0.1679753,0.5522504,0.0004014441,0.001038899,0.0003500567,0.02629676,0.1583266,0.02053038,0.01175509,0.01847671,0.0007662349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980919,0.00001300484,0.0001600115,0.00008386119,0.000005947565,0.0003512979,0.0001080286,0.00000276958,0.001183174],"genre_scores_gemma":[0.9936784,0.00006080915,0.002320305,0.0001854757,0.00001175469,0.0009403484,0.0001246789,0.000002087137,0.002676295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08233593,"threshold_uncertainty_score":0.1637133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06029169278726957,"score_gpt":0.3835990666178723,"score_spread":0.3233073738306027,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}